Chapter 1: What misleading statistic about AI pilots contributed to market sell-offs?
If you were following technology news in August last year, you almost certainly heard about this MIT study showing that 95% of generative AI pilots at companies were failing.
Chapter 2: What does the MIT report actually reveal about AI pilot success rates?
This result was big enough to contribute to a NASDAQ sell-off. People worked hard to come up with sophisticated explanations for how something this crazy could be true.
Chapter 3: What defines success in AI projects according to the study?
And it was repeated by Forbes, Axios, The Hill, the Harvard Business Review, and dozens of others, becoming a staple of elite opinion and one of the most enduring and widely cited statistics in the AI is overhyped backlash.
Chapter 4: How does the sample size affect the credibility of the AI study?
The problem?
Chapter 5: Why was the MIT report not available when it went viral?
The study behind these headlines is incredibly weak.
Chapter 6: What conflicts of interest are present in the MIT AI study?
Worse than you could imagine. And that headline is also a completely incorrect description of what it found, even taking the study entirely on its own terms. The story behind this study will demonstrate that whenever you see a juicy headline, even one with an attractive conclusion, and even one rewarding to come from MIT, it might just be complete nonsense.
Chapter 7: What is the real lesson learned from the AI pilot statistics?
The most important thing to know is that this report did not show that 95% of generative AI pilots at companies are failing, as almost all journalists claimed.
Rather, the report found that of all the organisations surveyed, 60% had investigated custom enterprise AI tools, 20% had gotten to the point of actually doing some pilot project with them, and 5% of the total had gone on to successfully deploy those tools in production. 80% of companies simply never piloted any custom task-specific generative AI.
Saying that 95% of them were failing is like saying 95% of Tinder users have failing marriages when 80% of the people you're talking about have never even gone on a date in the first place.
Moreover, according to their own survey, the primary reason why pilots didn't progress to deployment wasn't that they were going badly, but just the very familiar and generic organizational unwillingness to adopt new tools.
Now, the media is definitely at fault for putting a completely wrong number in their headlines, but the report also makes this mistake about its own graph, referring to a 95% failure rate for enterprise AI solutions. Wrong. These results actually show that among the 20% who did in fact pilot a custom AI tool, about 25% or a quarter were successful.
It's not really a low strike rate for a pilot project, and it's a success rate five times higher than what everyone was told about. And in reality, a 25% success rate is actually very impressive. Once you appreciate the bar, a project had to clear to count as a success in this study.
To qualify as a success, an AI application had to show a marked and sustained productivity or profit and loss impact within six months. They don't define marked or sustained, but a marked and sustained improvement in profitability or productivity within six months is obviously a high bar for any new project to clear.
It's widely understood that enterprise tech deployments often take years to show bottom line impacts, even if they're going quite well. And notice that by this standard, an AI project that merely breaks even, that's a failure. A project that has benefited the company in some way that hasn't yet markedly affected productivity or profits? Failure.
And a project that's on track to be profitable next year but isn't yet? Equally, a failure. Do you get the sense that maybe these authors would prefer to find that these projects aren't working? Well, we'll come back to that.
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